Adaptive signal processing
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Signal Processing Advances in Wireless and Mobile Communications, Volume 1: Trends in Channel Estimation and Equalization
Blind Channel Equalization and Identification
Blind Channel Equalization and Identification
Convergence of the IRWLS Procedure to the Support Vector Machine Solution
Neural Computation
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
Blind equalization of constant modulus signals using support vector machines
IEEE Transactions on Signal Processing
Super-exponential methods for blind deconvolution
IEEE Transactions on Information Theory
Blind identification and equalization based on second-order statistics: a time domain approach
IEEE Transactions on Information Theory
Blind channel identification based on second-order statistics: a frequency-domain approach
IEEE Transactions on Information Theory
Property restoral approach to blind equalization of digital transmission channels
IEEE Transactions on Consumer Electronics
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In this paper, using a common framework, we propose, analyze, and evaluate several variants of batch algorithms for blind equalization of SISO channels. They are based on the iterative re-weighted least square (IRWLS) solution for the support vector machine (SVM). The proposed methods combine the conventional cost function of the SVM with classical error functions applied to blind equalization: Sato's and Godard's error functions are included in the penalty term of the SVM. The relationship of these batch algorithms with conventional equalization and regularization techniques is analyzed in the paper. Simulation experiments performed over a relevant set of channels show that the proposed equalization methods perform better than traditional cumulant-based methods: they require a lower number of data samples to achieve the same equalization level and convergence ratio.